metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
topic_model_bert_topic
This is a BERTopic model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
Usage
To use this model, please install BERTopic:
pip install -U bertopic
You can use the model as follows:
from bertopic import BERTopic
topic_model = BERTopic.load("VegetaSama/topic_model_bert_topic")
topic_model.get_topic_info()
Topic overview
- Number of topics: 26
- Number of training documents: 10000
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | place - good - food - great - like | 82 | -1_place_good_food_great |
| 0 | store - like - car - just - great | 3133 | 0_store_like_car_just |
| 1 | mexican - tacos - food - salsa - good | 876 | 1_mexican_tacos_food_salsa |
| 2 | good - ordered - just - food - cheese | 725 | 2_good_ordered_just_food |
| 3 | pizza - good - crust - place - great | 568 | 3_pizza_good_crust_place |
| 4 | food - great - place - good - service | 536 | 4_food_great_place_good |
| 5 | hotel - room - pool - stay - airport | 445 | 5_hotel_room_pool_stay |
| 6 | burger - fries - burgers - good - like | 370 | 6_burger_fries_burgers_good |
| 7 | hair - dr - massage - nails - time | 339 | 7_hair_dr_massage_nails |
| 8 | coffee - starbucks - place - good - like | 269 | 8_coffee_starbucks_place_good |
| 9 | scottsdale - food - place - great - good | 262 | 9_scottsdale_food_place_great |
| 10 | sushi - roll - rolls - place - good | 258 | 10_sushi_roll_rolls_place |
| 11 | minutes - food - table - just - time | 241 | 11_minutes_food_table_just |
| 12 | ice - ice cream - cream - cupcakes - cupcake | 224 | 12_ice_ice cream_cream_cupcakes |
| 13 | breakfast - pancakes - eggs - good - place | 200 | 13_breakfast_pancakes_eggs_good |
| 14 | thai - curry - pad - food - pad thai | 199 | 14_thai_curry_pad_food |
| 15 | bbq - phoenix - food - brisket - good | 199 | 15_bbq_phoenix_food_brisket |
| 16 | beer - place - great - beers - food | 179 | 16_beer_place_great_beers |
| 17 | service - food - good - place - order | 152 | 17_service_food_good_place |
| 18 | pho - vietnamese - broth - spring - spring rolls | 147 | 18_pho_vietnamese_broth_spring |
| 19 | bar - music - place - night - cool | 106 | 19_bar_music_place_night |
| 20 | chinese - chinese food - food - soup - chicken | 101 | 20_chinese_chinese food_food_soup |
| 21 | greek - vegan - meat - food - place | 100 | 21_greek_vegan_meat_food |
| 22 | theater - movie - seats - movies - amc | 99 | 22_theater_movie_seats_movies |
| 23 | chinese - food - chinese food - pei - pei wei | 95 | 23_chinese_food_chinese food_pei |
| 24 | bar - good - food - happy - great | 95 | 24_bar_good_food_happy |
Training hyperparameters
- calculate_probabilities: True
- language: None
- low_memory: False
- min_topic_size: 10
- n_gram_range: (1, 1)
- nr_topics: None
- seed_topic_list: None
- top_n_words: 5
- verbose: True
- zeroshot_min_similarity: 0.7
- zeroshot_topic_list: None
Framework versions
- Numpy: 1.24.3
- HDBSCAN: 0.8.33
- UMAP: 0.5.5
- Pandas: 2.0.3
- Scikit-Learn: 1.3.0
- Sentence-transformers: 2.2.2
- Transformers: 4.32.1
- Numba: 0.58.1
- Plotly: 5.9.0
- Python: 3.11.5